Frayne, RichardAdair, David2017-07-252017-07-2520172017Adair, D. (2017). Computer-assisted Screening of Motion Artefact for Quality Control in Large-scale MR Imaging Trials (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25401http://hdl.handle.net/11023/3985As the scale of medical imaging trials increases, manual quality control of the enormous volume of imaging data becomes intractable and costly. Machine learning may provide solutions to reduce the challenge of these large trials through the development of computer-assisted screening tools. The objective of this dissertation was to evaluate the suitability of machine learning for solving scalability problems of manual quality control by training an automated classifier to detect simulated motion artefact on otherwise high-quality magnetic resonance images of healthy human brain. The classifier achieved high accuracy (98.5%) without any performance optimization, and, incidentally, discovered a screening error within the experiment dataset, further demonstrating the power of machine learning in this domain and encouraging further research towards computer-assisted screening tools.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.RadiologyArtificial IntelligenceComputer ScienceEngineering--Biomedicalmagnetic resonance imagingMachine Learningradiologyquality controlmotion artefactmaster thesis10.11575/PRISM/25401